Abstract

The rapid growth of social media platforms has increased the risk of unauthorized image usage, tampering, and reposting. This project introduces a hybrid image protection system utilizing invisible watermarking and perceptual hashing techniques. Invisible watermarking embeds ownership information discreetly within the image, ensuring authenticity without altering its visible content. Perceptual hashing detects tampering by comparing the image hash with the original. Together, these techniques safeguard images while providing users with a secure, intuitive interface for sharing and protecting their content.

Introduction

Digital media has revolutionized communication and social interaction, but it has also increased vulnerabilities to image manipulation and unauthorized distribution. Images can be easily altered, reposted, or stolen, leading to privacy breaches, reputational damage, and even legal consequences. This project addresses these challenges by developing a comprehensive image protection system that ensures ownership verification and tamper detection.

Problem Statement

The surge of image-sharing platforms has led to increased malicious activities such as morphing, unauthorized reposting, and tampering. Victims of image manipulation face reputational damage, emotional distress, and, in extreme cases, financial or personal harm. Current solutions are often technically complex or fail to provide real-time detection and comprehensive security for everyday users.

With the rise of social media platforms, image manipulation and unauthorized use have become prevalent issues. Current solutions often lack usability or fail to provide comprehensive security, leaving users vulnerable to digital content theft. There is a critical need for a user-friendly, efficient solution that protects images from tampering and unauthorized sharing.

Existing System and Disadvantages

Existing solutions include:

1. Digital Signatures: Ensure authenticity but are computationally complex.
2. Image Encryption: Secures images but limits usability for sharing.
3. Blockchain: Provides transparency but requires high resources and expertise.
4. Steganography: Hides information but lacks tampering detection.

Disadvantages:

– Lack of real-time tamper detection.
– High computational and resource requirements.
– Insufficient integration of ownership verification and alteration detection.

Proposed System and Advantages

The proposed system integrates:

1. Invisible Watermarking: Embeds discreet ownership information within the image without affecting its visible quality.
2. Perceptual Hashing: Generates content-based hashes to detect image manipulation or tampering.
3. Secure Login: Ensures only authorized users can access the system.
4. Web-Based Interface: Allows users to upload images, secure them, and verify tampering via an intuitive platform.

Advantages:

– Combines ownership verification and tamper detection.
– Real-time detection of image alterations.
– Simple and user-friendly interface.
– Resilient to social media compression and resizing techniques.

Modules

1. Login and Authentication Module: Ensures secure access to the system.
2. Image Watermarking Module: Embeds invisible watermarks into images.
3. Perceptual Hashing Module: Generates and verifies image hashes.
4. Tamper Detection Module: Identifies and reports image tampering.
5. Gallery Management Module: Displays and manages watermarked images.

Algorithms:

Invisible Watermarking:
– Modify pixel values subtly to embed watermark information without visible changes.

Perceptual Hashing:
– Generate unique content-based hashes for images. Compare hashes to detect alterations.

Software and Hardware Requirements

Software Requirements:
– Programming Language: Python
– Framework: Flask
– Libraries: Pillow, ImageHash, Flask-Session
– Browser Support: Modern browsers like Chrome, Firefox, and Edge

Hardware Requirements:
– Processor: Intel Core i3 or equivalent
– RAM: Minimum 4GB
– Storage: 10GB free space
– Operating System: Windows, macOS, or Linux